To generate a dataset for face recognition, a set of 100 realisations are generated for each of 10,000 different identities. The GAN is trained for 150k iterations, where-after there is no gain in the quality of images generated. The dataset is then prepared for face recognition training by aligning and cropping with MTCNN [1]} to \(112 \times 112\)  pixels. In comparison the dataset MS-Celeb-1M contains 6.46 million images from 94,682 identities [2]} and VGGFace2 contains 3.31 million images from 9,131 identities [3]}.
